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Here I visualize some macro variables under the impact of COVID-19
CO2raw <- read.xlsx(here('carbon-monitor-maingraphdatas.xlsx'),sheet = 1)
options(digits = 2)
options(scipen = 999)
CO2data <- CO2raw %>%
rename(CountryRegion = `country./.group.of.countries`) %>%
mutate(date = lubridate::dmy(CO2raw$date)) %>%
filter(!is.na(sector))
CO2_country_sector_dif <- CO2data %>%
group_by(CountryRegion,sector) %>%
#summarize(sum = sum(MtCO2.per.day)) %>%
mutate(dif_MtCO2.per.day = MtCO2.per.day - lag(MtCO2.per.day, default = 0),
year = factor(year(date)),
yday = as.Date(yday(date),'2020-01-01'))
What you might do wrong
#WRONG this is stacked
CO2_country_sector_dif %>%
filter(CountryRegion == 'China', sector == 'Power') %>%
ggplot() +
geom_area(aes(y = MtCO2.per.day, x = yday, color = year, fill = year,
alpha = 0.5)) +
theme_minimal() +
scale_x_date(date_labels = '%m/%d')
# How to use ggplot2 to plot a ribbon graph to show increase/decrease trends
#Using data empowered by Carbon Monitor (https://carbonmonitor.org/)
CO2_country_sector_dif %>%
filter(CountryRegion == 'China', sector == 'Power') %>%
ggplot(aes(y = MtCO2.per.day, x = yday)) +
geom_line(aes(color = year)) +
scale_color_manual(values = c("#00C1AA", "#FC717F")) +
theme_minimal()
CO2_country_sector_dif %>%
filter(CountryRegion == 'China', sector == 'Power') %>%
ggplot(aes(y = MtCO2.per.day, x = yday)) +
geom_line(aes(color = year)) +
geom_area(aes(fill = year, alpha = 0.5), position = position_dodge(0.8)) +
scale_color_manual(values = c("#00C1AA", "#FC717F")) +
scale_fill_manual(values = c("#00C1AA", "#FC717F")) +
theme_minimal()
CO2_country_sector_dif %>%
filter(CountryRegion == 'China', sector == 'Power') %>%
group_by(yday) %>%
mutate(min = min(MtCO2.per.day)) %>%
ggplot(aes(y = MtCO2.per.day, x = yday)) +
geom_line(aes(color = year)) +
# geom_area(aes(fill = year, alpha = 0.5), position = position_dodge(0.8)) +
geom_ribbon(aes(ymax = MtCO2.per.day, ymin = min, fill = year)) +
scale_color_manual(values = c("#00C1AA", "#FC717F")) +
scale_fill_manual(values = c("#00C1AA", "#FC717F")) +
theme_minimal()
final <- CO2_country_sector_dif %>%
filter(CountryRegion == 'China', sector == 'Power') %>%
group_by(yday) %>%
mutate(min = min(MtCO2.per.day)) %>%
ggplot(aes(y = MtCO2.per.day, x = yday)) +
geom_line(aes(color = year)) +
# geom_area(aes(fill = year, alpha = 0.5), position = position_dodge(0.8)) +
geom_ribbon(aes(ymax = MtCO2.per.day, ymin = min, fill = year)) +
scale_color_manual(values = c("#00C1AA", "#FC717F")) +
scale_fill_manual(values = c("#00C1AA", "#FC717F")) +
theme_minimal() +
scale_x_date(date_labels = '%m/%d', date_breaks = '1 months') +
scale_y_continuous(n.breaks = 10) +
ggtitle('China Power Sector') +
xlab('months')
final
#Interactive version
library(plotly)
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ggplotly(final)